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Axial Attention

59 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Axial Attention is a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. It was first proposed in CCNet [1] named as criss-cross attention, which harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Ho et al [2] extents CCNet to process multi-dimensional data. The proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. It serves as the basic building block for developing self-attention-based autoregressive models for high-dimensional data tensors, e.g., Axial Transformers. It has been applied in AlphaFold [3] for interpreting protein sequences.

[1] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, Wenyu Liu. CCNet: Criss-Cross Attention for Semantic Segmentation. ICCV, 2019.

[2] Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans. arXiv:1912.12180

[3] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A. Highly accurate protein structure prediction with AlphaFold. Nature. 2021 Jul 15:1-1.

Source: Axial Attention in Multidimensional Transformers

Papers archive 2025-07-28

30 shown of 59, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 91 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation11
Semantic Segmentation7
Deep Learning5
Generative Adversarial Network4
Image Segmentation4
Computational Efficiency3
Decoder3
Image Classification3
Medical Image Segmentation3
Object Detection3
SSIM3
Tumor Segmentation3
image-classification3
3D Object Detection2
Brain Tumor Segmentation2
Diagnostic2
Image Reconstruction2
Motion Estimation2
Position2
Relation2

Usage over time archive 2025-07-28

Papers per year tagged with Axial Attention: 2019 to 2025, peak 27 27 0 2019: 2 papers 2019 2020: 27 papers 2020 2021: 12 papers 2021 2022: 7 papers 2022 2023: 4 papers 2023 2024: 5 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (59 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Model Blocks

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